Assessment of surface water quality using multivariate statistical approaches: A case study of the Jialingjiang river basin, China

Assessment of surface water quality is a complex data- and- time-consuming task. In this study, the application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Jialingjiang river basin is presented. The dataset consists of analytical results in the river systems during from 2001 to 2005. Five physical and chemical parameters have been monitored on 36 key sampling sites on monthly basis. The dataset was treated using principal component analysis coupled to geostatistical analysis on principal components. Seven latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in surface water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in surface water quality for effective river water quality management.